Can I read Depression Recognition Using a Proposed Speech Chain Model Fusing Speech Production and Perception Features on EtoBox?

Depression Recognition Using a Proposed Speech Chain Model Fusing Speech Production and Perception Features by Minghao Du; Shuang Liu; Tao Wang; Wenquan Zhang; Yufeng Ke; Long Chen; Dong Ming is a Psychology article available to read on EtoBox.

What is Depression Recognition Using a Proposed Speech Chain Model Fusing Speech Production and Perception Features about?

Background Increasing depression patients puts great pressure on clinical diagnosis. Audio-based diagnosis is a helpful auxiliary tool for early mass screening. However, current methods consider only speech perception features, ignoring patients' vocal tract changes, which may partly result in the poor recognition. Methods This work proposes a novel machine speech chain model for depression recognition (MSCDR) that can capture text-independent depressive speech representation from the speaker's mouth to the listener's ear to improve recognition performance. In the proposed MSCDR, linear predictive coding (LPC) and Mel-frequency cepstral coefficients (MFCC) features are extracted to describe the processes of speech generation and of speech perception, respectively. Then, a one-dimensional convolutional neural network and a long short-term memory network sequentially capture intra- and inter-segment dynamic depressive features for classification. Results We tested the MSCDR on two public datasets with different languages and paradigms, namely, the Distress Analysis Interview Corpus-Wizard of Oz and the Multi-modal Open Dataset for Mental-disorder Analysis. The accuracy of the MSCDR o

Who reads Depression Recognition Using a Proposed Speech Chain Model Fusing Speech Production and Perception Features?

Author

Minghao Du; Shuang Liu; Tao Wang; Wenquan Zhang; Yufeng Ke; Long Chen; Dong Ming

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Psychology (Social Sciences)